A Novel Yardstick of Learning Time Spent in a Programming Language by Unpacking Bloom’s Taxonomy

Alcides Bernardo Tello, Ying Tien Wu, Tom Perry, Xu Yu-Pei

研究成果: 書貢獻/報告類型會議論文篇章同行評審


Instead of testing students, measuring precisely the total learning time spent by a student is of preponderant importance. Therefore, the goal of this article is to demonstrate the estimation of the time each student requires in mastering any topic content, until they become an expert. We have developed empirical evidence for this estimate based on Bloom’s taxonomy in a concrete case study at an engineering school by teaching loops in Python. Our result has shown that, on average, 4.98 hours are demanded in the “loop” lesson to reach the top level of Bloom’s taxonomy immediately after a half-hour lecture. Supported by Bloom’s taxonomy and the forgetting curve theory, the results of this study suggest that every student needs a different amount of time to master a topic via immediate post-lecture review, climbing the six levels of the aforementioned taxonomy; all pupils can learn and master anything at high levels but at very different rates. Schools should also readjust study plans to concentrate more time on level three and four of the taxonomy which demands the doing, designing, building and developing a particular domain of knowledge.

主出版物標題Intelligent Computing - Proceedings of the 2020 Computing Conference
編輯Kohei Arai, Supriya Kapoor, Rahul Bhatia
出版狀態已出版 - 2020
事件Science and Information Conference, SAI 2020 - London, United Kingdom
持續時間: 16 7月 202017 7月 2020


名字Advances in Intelligent Systems and Computing
1228 AISC


???event.eventtypes.event.conference???Science and Information Conference, SAI 2020
國家/地區United Kingdom


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